John C. Strong, Ph.D., is an associate research fellow at AbbVie Inc. with a degree in biochemical engineering and 15 years of experience in the pharmaceutical industry. He is most active in pharmaceutical materials science support and research, as well as modeling and scale-up support in drug product and drug process development and shelf life stability.
Development of new drug compounds can be described as high risk with potentially high return. Much of that risk has to do with the long development timeline—on the order of 10 years with total cost over $1 billion per marketed drug—and with the uncertainties along the way that can derail the process, such as clinical failures or manufacturing difficulties.
During the past decades, pharmaceutical companies have been paving their way to a new digital product design and development era. There have been many attempts to transform the business from purely experimental to one exploiting in silico virtual design, and several academic and industrial consortia are partnering closely to foster achieving this grand vision. One economic analysis indicated that the use of modeling and simulation tools in pharmaceutical development is producing a return on investment (ROI) of $4 to $10 per dollar invested. The greatest impact on ROI arose from employing/implementing modeling expertise in material sciences and drug substance development.
The major benefits of utilizing computational predictive models include improved quality products, reduced consumption of drug substance materials, improved experimental effectiveness, reduced time to market, and reduced risk for failures. In the January issue of the AAPS Newsmagazine, we discuss these models and the potential benefits of their application during development of pharmaceutical products. Read the article Predictive Modeling in Materials Science, Drug Product, and Process Development, from the Physical Pharmacy and Biopharmaceutics section of AAPS. Then participate in the discussion questions below.
What challenges do you see related to the use of predictive modeling in regulatory filings and how would you address them?
As more development activity risk is mitigated by predictive modeling, where do you ultimately see the balance between empirical data/experimentation and in silico modeling?
Where do you see future needs in predictive modeling given the trends in the pharmaceutical industry, and how would we prepare for them now?